The reliance on teamwork in organizations [1]}, coupled with recent advances in artificial intelligence [2]}, has generated a vision to develop collaborative Human-AI teams [3]}, [4]}. Human-AI teams promise to help overcome human biases and information processing limitations, reaching performance higher than human-only teams could [5]}, [6]}.
Despite some recent advances , [7]}, [8]} there remain significant difficulties in developing agents that interact with multiple, heterogeneous humans working on cognitive tasks engaged in cooperative communication in an ad-hoc team. Here, we draw on recent research of cognitive processes to explain collaborative decision making and develop Human-AI teams [9]}. To communicate efficiently, humans infer the beliefs, opinions, knowledge, and related states of mind of other people [10]}, [11]}. This is referred to as social perceptiveness or theory of mind [12]}. Recent research on collective intelligence, has provided a wide range of empirical evidence suggesting that ToM (and related processes governing collective memory, attention, and reasoning) is a significant predictor of human collective intelligence [13]}, [14]}, [15]}, [16]}. Indeed, ToM is especially beneficial for interdependent cognitive tasks that benefit when teams leverage their members’ expertise [17]}. Some work suggests that ToM is the mechanism that allow collectives to derive and utilize more performance-relevant information from their environment than a single individual without such connections could, for example, by facilitating balance between diversity and cognitive efficiency [18]}, [19]}.
As human civilization shifts further toward knowledge work [20]} where most value is realized if members fully use and integrate their unique expertise [21]}, this ability is increasingly important.
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